Global comparison of temperature and water stress representations in light use efficiency models for gross primary productivity estimation

Accurate estimation of gross primary productivity (GPP) using light use efficiency (LUE) models based on remote sensing data remains a challenge, because LUE determines environmental constraints within the modeling framework by incorporating temperature and water stress functions. Moreover, LUE models represent environmental stresses inconsistently. We conducted a global-scale comparison of different combinations of temperature and water stress functions to systematically evaluate the impact on GPP estimates. Monthly observation data from 172 eddy covariance flux towers distributed around the world were combined with six stress functions drawn from three prominent LUE models (the Carnegie–Ames–Stanford Approach (CASA), Vegetation Photosynthesis Model (VPM), and Moderate Resolution Imaging Spectroradiometer) to develop nine candidate schemes, and the model performance was evaluated according to the coefficient of determination (R²) and root mean square error (RMSE). Globally, the best‑performing configuration coupled the water stress function from VPM with the temperature stress function from CASA (R² = 0.721; RMSE = 55.4  g C·m⁻²·month⁻¹). However, the model performance markedly varied with the vegetation types: temperature stresses were the principal limiting factor in forests, croplands, and wetlands, whereas water stresses were the principal limiting factor in arid and temperate grasslands. A feature importance analysis using the XGBoost algorithm corroborated this pattern. The differences among stress functions mainly originated from their input parameters. A sensitivity analysis revealed that GPP is most responsive to changes in the optimum temperature compared with water or temperature extremes. These findings underscore the need to tailor stress parameterization to specific climate zones and vegetation types. They provide clear guidance for improving GPP estimates based on remote sensing products and lay a foundation for the next generation of carbon‑cycle models to consider the effects of climate change.